Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

Facilitating the application of machine learning (ML) to materials science problems requires
enhancing the data ecosystem to enable discovery and collection of data from many …

The fields of brain‐inspired computing, robotics, and, more broadly, artificial intelligence (AI)
seek to implement knowledge gleaned from the natural world into human‐designed …

Low signal-to-noise ratio (SNR) measurements, primarily due to the quartic attenuation of
intensity with distance, are arguably the fundamental barrier to real-time, high-resolution …

Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s.
Online analysis methods are needed to enable the collection of only interesting subsets of …

Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical
x-ray optics. Recovering images from raw ptychographic data, however, requires the …

Neuromorphic computing and artificial intelligence hardware generally aims to emulate
features found in biological neural circuit components and to enable the development of …

We review the growing use of machine learning in electron microscopy (EM) driven in part
by the availability of fast detectors operating at kiloHertz frame rates leading to large data …